scholarly journals Mexican Axolotl Optimization: A Novel Bioinspired Heuristic

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 781
Author(s):  
Yenny Villuendas-Rey ◽  
José L. Velázquez-Rodríguez ◽  
Mariana Dayanara Alanis-Tamez ◽  
Marco-Antonio Moreno-Ibarra ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Computational Complexity ◽  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Cuckoo Search ◽  
Search Space ◽  
Monarch Butterfly ◽  
Mexican Axolotl ◽  
Dragonfly Algorithm ◽  
Whale Optimization ◽  
Exact Optimization

When facing certain problems in science, engineering or technology, it is not enough to find a solution, but it is essential to seek and find the best possible solution through optimization. In many cases the exact optimization procedures are not applicable due to the great computational complexity of the problems. As an alternative to exact optimization, there are approximate optimization algorithms, whose purpose is to reduce computational complexity by pruning some areas of the problem search space. To achieve this, researchers have been inspired by nature, because animals and plants tend to optimize many of their life processes. The purpose of this research is to design a novel bioinspired algorithm for numeric optimization: the Mexican Axolotl Optimization algorithm. The effectiveness of our proposal was compared against nine optimization algorithms (artificial bee colony, cuckoo search, dragonfly algorithm, differential evolution, firefly algorithm, fitness dependent optimizer, whale optimization algorithm, monarch butterfly optimization, and slime mould algorithm) when applied over four sets of benchmark functions (unimodal, multimodal, composite and competition functions). The statistical analysis shows the ability of Mexican Axolotl Optimization algorithm of obtained very good optimization results in all experiments, except for composite functions, where the Mexican Axolotl Optimization algorithm exhibits an average performance.

Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

2020 ◽  
pp. 1-12
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jialing Tang
Keyword(s):  
Lateral Inhibition ◽  
Optimization Algorithm ◽  
Image Matching ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Inhibition Mechanism ◽  
Whale Optimization ◽  
Accuracy And Stability

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

scholarly journals A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

scholarly journals Insights into Exploration and Exploitation Power of Optimization Algorithm Using DSCTool

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1474
Author(s):  
Peter Korošec ◽  
Tome Eftimov
Keyword(s):  
Statistical Analysis ◽  
Web Service ◽  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Search Space ◽  
Exploration And Exploitation ◽  
Learning Tool ◽  
Additional Information ◽  
E Learning ◽  
Single Objective

When making statistical analysis of single-objective optimization algorithms’ performance, researchers usually estimate it according to the obtained optimization results in the form of minimal/maximal values. Though this is a good indicator about the performance of the algorithm, it does not provide any information about the reasons why it happens. One possibility to get additional information about the performance of the algorithms is to study their exploration and exploitation abilities. In this paper, we present an easy-to-use step by step pipeline that can be used for performing exploration and exploitation analysis of single-objective optimization algorithms. The pipeline is based on a web-service-based e-Learning tool called DSCTool, which can be used for making statistical analysis not only with regard to the obtained solution values but also with regard to the distribution of the solutions in the search space. Its usage does not require any special statistic knowledge from the user. The gained knowledge from such analysis can be used to better understand algorithm’s performance when compared to other algorithms or while performing hyperparameter tuning.

scholarly journals Modified chemical reaction optimization and its application in engineering problems

2021 ◽  
Vol 18 (6) ◽  
pp. 7143-7160
Author(s):  
Shijing Ma ◽  
◽  
Yunhe Wang ◽  
Shouwei Zhang ◽  
Keyword(s):  
Chemical Reactions ◽  
Chemical Reaction ◽  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Differential Evolution Algorithm ◽  
Search Space ◽  
Evolutionary Optimization ◽  
Chemical Reaction Optimization ◽  
Reaction Optimization ◽  
Proposed Model

<abstract><p>Chemical Reaction Optimization (CRO) is a simple and efficient evolutionary optimization algorithm by simulating chemical reactions. As far as the current research is concerned, the algorithm has been successfully used for solving a number of real-world optimization tasks. In our paper, a new real encoded chemical reaction optimization algorithm is proposed to boost the efficiency of the optimization operations in standard chemical reactions optimization algorithm. Inspired by the evolutionary operation of the differential evolution algorithm, an improved search operation mechanism is proposed based on the underlying operation. It is modeled to further explore the search space of the algorithm under the best individuals. Afterwards, to control the perturbation frequency of the search strategy, the modification rate is increased to balance between the exploration ability and mining ability of the algorithm. Meanwhile, we also propose a new population initialization method that incorporates several models to produce high-quality initialized populations. To validate the effectiveness of the algorithm, nine unconstrained optimization algorithms are used as benchmark functions. As observed from the experimental results, it is evident that the proposed algorithm is significantly better than the standard chemical reaction algorithm and other evolutionary optimization algorithms. Then, we also apply the proposed model to address the synthesis problem of two antenna array synthesis. The results also reveal that the proposed algorithm is superior to other approaches from different perspectives.</p></abstract>

scholarly journals Near Infrared and Visible Image Registration using Whale Optimization Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Image Registration ◽  
Optimization Algorithm ◽  
Near Infrared ◽  
Optimization Problems ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Visible Image ◽  
Local Optima ◽  
Multimodal Image Registration ◽  
Whale Optimization

This paper reports the use of a nature-inspired metaheuristic algorithm known as ‘Whale Optimization Algorithm’ (WOA) for multimodal image registration. WOA is based on the hunting behaviour of Humpback whales and provides better exploration and exploitation of the search space with small possibility of trapping in local optima. Though WOA is used in various optimization problems, no detailed study is available for its use in image registration. For this study different sets of NIR and visible images are considered. The registration results are compared with the other state of the art image registration methods. The results show that WOA is a very competitive algorithm for NIR-visible image registration. With the advantages of better exploration of search space and local optima avoidance, the algorithm can be a suitable choice for multimodal image registration.

scholarly journals A Combination Approach of Two Metaheuristic Algorithm for Optimal Feature Selection: Case Study Email Spam Detection

2020 ◽  
Author(s):  
Hekmat Mohmmadzadeh
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Selection Problem ◽  
Second Step ◽  
Feature Selection Problem ◽  
Flower Pollination ◽  
Opposition Based Learning ◽  
Whale Optimization

Selecting a feature in data mining is one of the most challenging and important activities in pattern recognition. The issue of feature selection is to find the most important subset of the main features in a specific domain, the main purpose of which is to remove additional or unrelated features and ultimately improve the accuracy of the categorization algorithms. As a result, the issue of feature selection can be considered as an optimization problem and to solve it, meta-innovative algorithms can be used. In this paper, a new hybrid model with a combination of whale optimization algorithms and flower pollination algorithms is presented to address the problem of feature selection based on the concept of opposition-based learning. In the proposed method, we tried to solve the problem of optimization of feature selection by using natural processes of whale optimization and flower pollination algorithms, and on the other hand, we used opposition-based learning method to ensure the convergence speed and accuracy of the proposed algorithm. In fact, in the proposed method, the whale optimization algorithm uses the bait siege process, bubble attack method and bait search, creates solutions in its search space and tries to improve the solutions to the feature selection problem, and along with this algorithm, Flower pollination algorithm with two national and local search processes improves the solution of the problem selection feature in contrasting solutions with the whale optimization algorithm. In fact, we used both search space solutions and contrasting search space solutions, all possible solutions to the feature selection problem. To evaluate the performance of the proposed algorithm, experiments are performed in two stages. In the first phase, experiments were performed on 10 sets of data selection features from the UCI data repository. In the second step, we tried to test the performance of the proposed algorithm by detecting spam emails. The results obtained from the first step show that the proposed algorithm, by running on 10 UCI data sets, has been able to be more successful in terms of average selection size and classification accuracy than other basic meta-heuristic algorithms. Also, the results obtained from the second step show that the proposed algorithm has been able to perform spam emails more accurately than other similar algorithms in terms of accuracy by detecting spam emails.

scholarly journals Multi-Objective Hybrid Optimization Algorithm Using a Comprehensive Learning Strategy for Automatic Train Operation

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1882
Author(s):  
Longda Wang ◽  
Xingcheng Wang ◽  
Kaiwei Liu ◽  
Zhao Sheng
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimization Algorithms ◽  
Hybrid Optimization ◽  
Multi Objective ◽  
Chaotic Mapping ◽  
Hybrid Optimization Algorithm ◽  
Whale Optimization ◽  
Train Operation ◽  
Automatic Train Operation

Aiming at the problem of easy-to-fall-into local convergence for automatic train operation (ATO) velocity ideal trajectory profile optimization algorithms, an improved multi-objective hybrid optimization algorithm using a comprehensive learning strategy (ICLHOA) is proposed. Firstly, an improved particle swarm optimization algorithm which adopts multiple particle optimization models is proposed, to avoid the destruction of population diversity caused by single optimization model. Secondly, to avoid the problem of random and blind searching in iterative computation process, the chaotic mapping and the reverse learning mechanism are introduced into the improved whale optimization algorithm. Thirdly, the improved archive mechanism is used to store the non-dominated solutions in the optimization process, and fusion distance is used to maintain the diversity of elite set. Fourthly, a dual-population evolutionary mechanism using archive as an information communication medium is designed to enhance the global convergence improvement of hybrid optimization algorithms. Finally, the optimization results on the benchmark functions show that the ICLHOA can significantly outperform other algorithms for contrast. Furthermore, the ATO Matlab/simulation and hardware-in-the-loop simulation (HILS) results show that the ICLHOA has a better optimization effect than that of the traditional optimization algorithms and improved algorithms.

scholarly journals A Node Location Method in Wireless Sensor Networks Based on a Hybrid Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jeng-Shyang Pan ◽  
Fang Fan ◽  
Shu-Chuan Chu ◽  
Zhigang Du ◽  
Huiqi Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Search Space ◽  
Wireless Sensor ◽  
Practical Applications ◽  
Hybrid Optimization Algorithm ◽  
Whale Optimization ◽  
Node Location ◽  
Received Signal Strength Indication

Wireless sensor networks (WSN) have gradually integrated into the concept of the Internet of Things (IoT) and become one of the key technologies. This paper studies the optimization algorithm in the field of artificial intelligence (AI) and effectively solves the problem of node location in WSN. Specifically, we propose a hybrid algorithm WOA-QT based on the whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm. It skillfully combines the strengths of the two algorithms, not only retaining the WOA’s distinctive framework advantages but also having QUATRE’s excellent coevolution ability. In order to further save optimization time, an auxiliary strategy for dynamically shrinking the search space (DSS) is introduced in the algorithm. To ensure the fairness of the evaluation, this paper selects 30 different types of benchmark functions and conducts experiments from multiple angles. The experiment results demonstrate that the optimization quality and efficiency of WOA-QT are very prominent. We use the proposed algorithm to optimize the weighted centroid location (WCL) algorithm based on received signal strength indication (RSSI) and obtain satisfactory positioning accuracy. This reflects the high value of the algorithm in practical applications.

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